Eye Blink Rate Research Articles

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Spontaneous eye blinking as a diagnostic and prognostic marker in disorders of consciousness: Protocol of an international multicentre longitudinal study.

Diagnostic and prognostic decision-making in patients with Disorders of Consciousness (DoC) is challenging. It has been suggested that spontaneous eye blink rate is an index of patients' level of consciousness easy to detect in clinical practice. Further blinking features (i.e., amplitude, duration, variability in intervals between blinks) may change as a function of cognitive load, but have not been investigated in patients with DoC. This multicentre, longitudinal study aims at exploring the diagnostic and prognostic value of spontaneous eye blinking features in DoC. Eight European medical institutions will enrol consecutively admitted adult patients with DoC. Within two weeks from study entry demographic, anamnestic and clinical data will be collected. Moreover, patients will undergo two 20-minute EEG-EOG recordings at rest, to collect blinking features and EEG activity. A clinical follow-up will be performed after 6 months. A group of healthy individuals will be enrolled for reference. Possible differences in blink features between patients and the reference group, differences across diagnostic sub-groups, and correlations between blinking features and clinical outcome will be investigated. The results of this study might help clinicians to reduce misdiagnosis rate in DoC and provide useful information for prognostication and care pathway plan.

Changes in the eye blink and half-blink rates in horses as a reaction to a stressor: A preliminary study

Changes in the eye blink and half-blink rates in horses as a reaction to a stressor: A preliminary study

The interactive effects of drawing task, prompt format, and visual aids on video-based learning: Attention and drawing accuracy as mediators

This study investigated the effects of open- and closed-book learning conditions either accompanied by visual aids or without on learners’ attention, drawing accuracy, and learning performance. Participants were 127 undergraduate learners randomly assigned to view either an open- or closed-book prompt, who then watched either a video lecture accompanied by visual aids or one without (i.e. visuals provided vs. no visuals). The results showed that for learners who were provided with visual aids, compared to the open-book condition, those in the closed-book condition demonstrated higher eye blink rate as well as improved immediate and delayed test scores. However, this advantage was not observed in the condition with no visual materials. Mediation analysis showed that increased immediate test scores in the visuals provided condition was due partly to improved drawing accuracy. Multiple mediation analyses showed that the open-book with visuals provided condition led to lower eye blink rate, increased fixation count, and improved drawing accuracy, which collectively resulted in decreased immediate test performance. These findings have implications for educators and instructional designers regarding how to tailor effective learning strategies; specifically, educators could consider using closed-book learning conditions with visual aids to enhance learners’ attention and improve their learning performance.

Spontaneous eye blinks in horses (Equus caballus) are modulated by attention

Spontaneous eye blinks are brief closures of both eyelids. The spontaneous eye blink rate (SEBR) exceeds physiological corneal needs and is modulated by emotions and cognitive states, including vigilance and attention, in humans. In several animal species, the SEBR is modulated by stress and antipredator vigilance, which may limit the loss of visual information due to spontaneous eye closing. Here, we investigated whether the SEBR is modulated by attention in the domestic horse (Equus caballus). Our data supported previous studies indicating a tonic SEBR specific to each individual. We also found that, superimposed on a tonic SEBR, phasic changes were induced by cognitive processing. Attention downmodulated the SEBR, with the magnitude of blink inhibition proportional to the degree of attentional selectivity. On the other hand, reward anticipation upregulated the SEBR. Our data also suggested that horses possess the cognitive property of object permanence: they understand that an object that is no longer in their visual field has not ceased to exist. In conclusion, our results suggested that spontaneous eye blinks in horses are modulated by attentional cognitive processing.

Attentional Bias, Pupillometry, and Spontaneous Blink Rate: Eye Characteristic Assessment Within a Translatable Nicotine Cue Virtual Reality Paradigm.

Incentive salience processes are important for the development and maintenance of addiction. Eye characteristics such as gaze fixation time, pupil diameter, and spontaneous eyeblink rate (EBR) are theorized to reflect incentive salience and may serve as useful biomarkers. However, conventional cue exposure paradigms have limitations that may impede accurate assessment of these markers. This study sought to evaluate the validity of these eye-tracking metrics as indicators of incentive salience within a virtual reality (VR) environment replicating real-world situations of nicotine and tobacco product (NTP) use. NTP users from the community were recruited and grouped by NTP use patterns: nondaily (n=33) and daily (n=75) use. Participants underwent the NTP cue VR paradigm and completed measures of nicotine craving, NTP use history, and VR-related assessments. Eye-gaze fixation time (attentional bias) and pupillometry in response to NTP versus control cues and EBR during the active and neutral VR scenes were recorded and analyzed using ANOVA and analysis of covariance models. Greater subjective craving, as measured by the Tobacco Craving Questionnaire-Short Form, following active versus neutral scenes was observed (F1,106=47.95; P<.001). Greater mean eye-gaze fixation time (F1,106=48.34; P<.001) and pupil diameter (F1,102=5.99; P=.02) in response to NTP versus control cues were also detected. Evidence of NTP use group effects was observed in fixation time and pupillometry analyses, as well as correlations between these metrics, NTP use history, and nicotine craving. No significant associations were observed with EBR. This study provides additional evidence for attentional bias, as measured via eye-gaze fixation time, and pupillometry as useful biomarkers of incentive salience, and partially supports theories suggesting that incentive salience diminishes as nicotine dependence severity increases.

The association between physiological and eye-tracking metrics and cognitive load in drivers: A meta-analysis

Driving performance can be impaired by a high cognitive load of drivers. Thus, it is important to estimate drivers’ cognitive load. Although physiological and eye-tracking metrics have been widely used in many studies to assess cognitive load while driving, conflicts still exist regarding the association between physiological and eye-tracking metrics and different levels of cognitive load. Through a meta-analysis, our study aims to quantify the association between physiological, eye-tracking metrics and cognitive load induced by n-back tasks. A total of 18 articles met the inclusion criteria for the meta-analysis. The results indicate four types of metrics, including the sensitive-to-low ones that can only differentiate the low to medium level of cognitive load (i.e., the power spectrum of θ wave of electroencephalogram at Fp1 channel); high-resolution ones that can differentiate all levels of cognitive load (including pupil size, heart rate, and skin conductance); and low-resolution ones that can only differentiate low and high cognitive load (including the total power spectrum of electrocardiogram, eye blink rate, and respiration rate) and others (the power spectrum of θ wave of electroencephalogram at Fp2 channel). Furthermore, the association between metrics and cognitive load can be modulated by the n-back version, modality of n-back task, automation level, and percentage of male participants. In summary, this study contributes to the literature by quantifying associations between physiological and eye-tracking metrics and different cognitive load levels. Practically, we provide evidence for the selection of physiological and eye-tracking metrics for future driving cognitive load monitoring system design.

Altered pupil light and darkness reflex and eye-blink responses in late-life depression

BackgroundLate-life depression (LLD) is a prevalent neuropsychiatric disorder in the older population. While LLD exhibits high mortality rates, depressive symptoms in older adults are often masked by physical health conditions. In younger adults, depression is associated with deficits in pupil light reflex and eye blink rate, suggesting the potential use of these responses as biomarkers for LLD.MethodsWe conducted a study using video-based eye-tracking to investigate pupil and blink responses in LLD patients (n = 25), older (OLD) healthy controls (n = 29), and younger (YOUNG) healthy controls (n = 25). The aim was to determine whether there were alterations in pupil and blink responses in LLD compared to both OLD and YOUNG groups.ResultsLLD patients displayed significantly higher blink rates and dampened pupil constriction responses compared to OLD and YOUNG controls. While tonic pupil size in YOUNG differed from that of OLD, LLD patients did not exhibit a significant difference compared to OLD and YOUNG controls. GDS-15 scores in older adults correlated with light and darkness reflex response variability and blink rates. PHQ-15 scores showed a correlation with blink rates, while MoCA scores correlated with tonic pupil sizes.ConclusionsThe findings demonstrate that LLD patients display altered pupil and blink behavior compared to OLD and YOUNG controls. These altered responses correlated differently with the severity of depressive, somatic, and cognitive symptoms, indicating their potential as objective biomarkers for LLD.

Arousal, interindividual differences and temporal binding a psychophysiological study

The sense of agency varies as a function of arousal in negative emotional contexts. As yet, it is unknown whether the same is true for positive affect, and how inter-individual characteristics might predict these effects. Temporal binding, an implicit measure of the sense of agency, was measured in 59 participants before and after watching either an emotionally neutral film clip or a positive film clip with high or low arousal. Analyses included participants’ individual differences in subjective affective ratings, physiological arousal (pupillometry, skin conductance, heart rate), striatal dopamine levels via eye blink rates, and psychopathy. Linear mixed models showed that sexual arousal decreased temporal binding whereas calm pleasure had no facilitation effect on binding. Striatal dopamine levels were positively linked whereas subjective and physiological arousal may be negatively associated with binding towards actions. Psychopathic traits reduced the effect of high arousal on binding towards actions. These results provide evidence that individual differences influence the extent to which the temporal binding is affected by high arousing states with positive valence.

What is a blink? Classifying and characterizing blinks in eye openness signals

Blinks, the closing and opening of the eyelids, are used in a wide array of fields where human function and behavior are studied. In data from video-based eye trackers, blink rate and duration are often estimated from the pupil-size signal. However, blinks and their parameters can be estimated only indirectly from this signal, since it does not explicitly contain information about the eyelid position. We ask whether blinks detected from an eye openness signal that estimates the distance between the eyelids (EO blinks) are comparable to blinks detected with a traditional algorithm using the pupil-size signal (PS blinks) and how robust blink detection is when data quality is low. In terms of rate, there was an almost-perfect overlap between EO and PS blink (F1 score: 0.98) when the head was in the center of the eye tracker’s tracking range where data quality was high and a high overlap (F1 score 0.94) when the head was at the edge of the tracking range where data quality was worse. When there was a difference in blink rate between EO and PS blinks, it was mainly due to data loss in the pupil-size signal. Blink durations were about 60 ms longer in EO blinks compared to PS blinks. Moreover, the dynamics of EO blinks was similar to results from previous literature. We conclude that the eye openness signal together with our proposed blink detection algorithm provides an advantageous method to detect and describe blinks in greater detail.

An efficient multi-task learning CNN for driver attention monitoring

Driver Monitoring System (DMS), usually equipped with a camera, is an emerging vehicle safety system that can monitor driver attentiveness and trigger timely alarms when signs of inattention are detected. Since a single indicator (e.g., eye blink rate) is insufficient and unreliable to analyze driver attentiveness, almost all existing solutions train several independent models to identify driver facial states, such as face landmark, head pose, yawning, eye state, etc. However, apart from neglecting the inherent correlations between these related tasks, multiple models also raise challenges for vehicle safety-critical systems (e.g., hardware resources, software compatibility, and real-time response). In this paper, we propose a multi-task learning CNN framework (DANet) to unify the relevant tasks into one model and simultaneously output various driver facial states. By sharing the common features and parameters of highly related tasks, DANet avoids repetitive computations and mitigates single task overfitting. More importantly, the model provides a comprehensive overview of facial states while maintaining low complexity. We also propose two novel designs: (1) Dual-loss Block, which decomposes the pose estimation task into pose classification and coarse-to-fine regression; (2) Head Pose Penalization, which constrains the network to predict gaze direction based on predicted head pose. Our method achieves compelling results in both speed and accuracy on a vehicle computing platform, marking a momentous step in this field.

Insights from Eye Blinks into the Cognitive Processes Involved in Visual Word Recognition.

Behavioral differences in speed and accuracy between reading familiar and unfamiliar words are well-established in the empirical literature. However, these standard measures of skill proficiency are limited in their ability to capture the moment-to-moment processing involved in visual word recognition. In the present study, the effect of word familiarity was initially investigated using an eye blink rate among adults and children. The probability of eye blinking was higher for familiar (real) words than for unfamiliar (pseudo)words. This counterintuitive pattern of results suggests that the processing of unfamiliar (pseudo)words is more demanding and perhaps less rewarding than the processing of familiar (real) words, as previously observed in both behavioral and pupillometry data. Our findings suggest that the measurement of eye blinks might shed new light on the cognitive processes involved in visual word recognition and other domains of human cognition.

Eye understand: Physiological measures as novel predictors of adaptive learning in horses

Striatal dopamine is a neurotransmitter that marks reward and mediates reward learning. Spontaneous Eye Blink Rate (SEBR) reflects striatal dopamine activity and could offer a novel, proxy measure of learning performance. Additionally, arousal affects performance in a range of cognitive tasks, but there is less evidence for the relationship between baseline arousal and learning. This study investigated whether SEBR predicts learning performance in an equine model. Further, it investigated novel physiological predictors of learning performance (eye temperature and heart rate variability). Nineteen horses completed a two-choice discrimination reversal learning (RL) task. Performance was measured using a Performance Index. SEBR and Heart Rate Variability (HRV) were measured at rest (home stable) and during RL. Infrared Thermography of eye temperature (IRT) was measured immediately before and after RL. SEBR did not change in response to RL despite being thought to reflect striatal dopamine activity. HRV was higher during RL than at rest though was not statistically significant (p = 0.06). Additionally, eye temperature decreased significantly during trials (left eye: p = 0.002; right eye: p = 0.05). These results indicate lower arousal in response to training, possibly similar to a ‘flow state’ in humans. Results of a negative binomial GLM revealed that SEBR was not associated with performance, however, positive predictors of learning performance included resting HRV (p = 0.009), HRV during the task (p = 0.002), and left eye temperature change (p < 0.0001). The association between left eye temperature and learning performance is consistent with lateralised blood flow to the left-brain hemisphere (responsible for learning targeted responses) and thought to be the first observation of this phenomenon during learning. Lower arousal may facilitate left-hemisphere dominance, creating the cognitive space to respond adaptively in RL. Left hemispheric dominance controls parasympathetic activity, potentially explaining the interactions observed here. Results suggest arousal both in the home and training environment may have important impacts on learning.

Visual analysis of fatigue in Industry 4.0

The performance of manufacturing operations relies heavily on the operators’ performance. When operators begin to exhibit signs of fatigue, both their individual performance and the overall performance of the manufacturing plant tend to decline. This research presents a methodology for analyzing fatigue in assembly operations, considering indicators such as the EAR (Eye Aspect Ratio) indicator, operator pose, and elapsed operating time. To facilitate the analysis, a dataset of assembly operations was generated and recorded from three different perspectives: frontal, lateral, and top views. The top view enables the analysis of the operator’s face and posture to identify hand positions. By labeling the actions in our dataset, we train a deep learning system to recognize the sequence of operator actions required to complete the operation. Additionally, we propose a model for determining the level of fatigue by processing multimodal information acquired from various sources, including eye blink rate, operator pose, and task duration during assembly operations.

MmDrive: Fine-grained Fatigue Driving Detection Using mmWave Radar

Early detection of fatigue driving is pivotal for the safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle the above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide the driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about 96%, and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21 bpm , 0.54 bpm , and 2.52 bpm , respectively. And the accuracy of the fatigue detection algorithm we proposed reached 97.63%.

The HPA and SAM axis mediate the impairment of creativity under stress.

With the ever-changing social environment, individual creativity is facing a severe challenge induced by stress. However, little is known regarding the underlying mechanisms by which acute stress affects creative cognitive processing. The current research explored the impacts of the neuroendocrine response on creativity under stress and its underlying cognitive flexibility mechanisms. The enzyme-linked immuno sorbent assay was employed to assess salivary cortisol, which acted as a marker of stress-induced activation of the hypothalamic-pituitary-adrenal (HPA) axis. Eye blink rate (EBR) and pupil diameter were measured as respective indicators of dopamine and noradrenaline released by the activation of the sympathetic-adrenal-medullary (SAM) axis. The Wisconsin card task (WCST) measured cognitive flexibility, while the alternative uses task (AUT) and the remote association task (RAT) measured separately divergent and convergent thinking in creativity. Results showed higher cortisol increments following acute stress induction in the stress group than control group. Ocular results showed that the stress manipulation significantly increased EBR and pupil diameter compared to controls, reflecting increased SAM activity. Further analysis revealed that stress-released cortisol impaired the originality component of the AUT, reducing cognitive flexibility as measured by perseverative errors on the WCST task. Serial mediation analyses showed that both EBR and pupil diameter were also associated with increased perseverative errors leading to poor originality on the AUT. These findings confirm that physiological arousal under stress can impair divergent thinking through the regulation of different neuroendocrine pathways, in which the deterioration of flexible switching plays an important mediating role.

Deception/Truthful Prediction Based on Facial Feature and Machine Learning Analysis

The Automatic Deception detection refers to the investigative practices used to determine whether person is telling you Truth or lie. Automatic deception detection has been studied extensively as it can be useful in many real-life scenarios in health, justice, and security systems. Many psychological studies have been reported for deception detection. Polygraph testing is a current trending technique to detect deception, but it requires human intervention and training. In recent times, many machine learning based approaches have been applied to detect deceptions. Various modalities like Thermal Imaging, Brain Activity Mapping, Acoustic analysis, eye tracking. Facial Micro expression processing and linguistic analyses are used to detect deception. Machine learning techniques based on facial feature analysis look like a promising path for automatic deception detection. It also works without human intervention. So, it may give better results because it does not affect race or ethnicity. Moreover, one can do covert operation to find deceit using facial video recording. Covert Operation may capture the real personality of deceptive persons. By making combination of various facial features like Facial Emotion, Facial Micro Expressions and Eye blink rate, pupil size, Facial Action Units we can get better accuracy in Deception Detection.

Design and Usability Study of a Point of Care mHealth App for Early Dry Eye Screening and Detection.

Significantly increased eye blink rate and partial blinks have been well documented in patients with dry eye disease (DED), a multifactorial eye disorder with few effective methods for clinical diagnosis. In this study, a point of care mHealth App named "EyeScore" was developed, utilizing blink rate and patterns as early clinical biomarkers for DED. EyeScore utilizes an iPhone for a 1-min in-app recording of eyelid movements. The use of facial landmarks, eye aspect ratio (EAR) and derivatives enabled a comprehensive analysis of video frames for the determination of eye blink rate and partial blink counts. Smartphone videos from ten DED patients and ten non-DED controls were analyzed to optimize EAR-based thresholds, with eye blink and partial blink results in excellent agreement with manual counts. Importantly, a clinically relevant algorithm for the calculation of "eye healthiness score" was created, which took into consideration eye blink rate, partial blink counts as well as other demographic and clinical risk factors for DED. This 10-point score can be conveniently measured anytime with non-invasive manners and successfully led to the identification of three individuals with DED conditions from ten non-DED controls. Thus, EyeScore can be validated as a valuable mHealth App for early DED screening, detection and treatment monitoring.

A-355 Eyescore, A Point of Care Smartphone App for Early Dry Eye Diagnosis and Management

Abstract Current diagnosis of dry eye disease (DED) has significant challenges with limited accuracy and poor correlation of clinical symptoms. The goal of this study is to develop a smartphone App, namely “EyeScore”, to provide a point of care (POC) and digital solution for early diagnosis of DED. The authors tested the hypothesis of measuring eye blink rate and partial blink count as early clinical biomarkers for the calculation of so called “eye healthiness score”, which allows a convenient, rapid eye exam at home with low resource settings. EyeScore uses an iPhone as an imaging and sensing device for in-App recordings of eyelid movements. The use of facial landmark recognition and eye aspect ratio (EAR) enabled comprehensive digital analysis of video frames for determination of the eye opening/closed states. The smartphone videos from 10 DED patients and 10 healthy controls were tested to optimize EAR derived thresholds for accurate measurements of blink rates and partial blink counts. The authors formulated a clinically relevant algorithm for the calculation of “eye healthiness score”. This 10-point scale score can be easily measured anytime with a non-invasive manner and remotely shared with the patient's eye doctor. As a result, both patients and doctors can monitor the eye conditions over time. Our results showed that EyeScore confirmed the diagnosis of all 10 DED patients. Importantly, Eyescore also identified three individuals with “pre-DED” conditions from 10 healthy controls, demonstrating its potential clinical values for early diagnosis and management of DED. Materials and Methods An Apple MacBook Pro installed with iOS software (iOS 16.1.2) and Xcode 14.0 was used for coding and debugging. Visionkit SDK from Apple Developer was adopted to extract coordinate data from six eye facial landmarks. EAR values were calculated based on the previously published formula, with a dynamic EAR developed as a threshold for the measurement of blink rate and half blink count. For eye healthiness score, a 10-point formula was formulated (0–3, normal; 4–5, Pre-DED; 6–10, DED) with several contributing factors including blink rate, half blink count, eye discomfort and demographic characteristics. Results A total of 20 iPhone videos from DED patients and healthy controls were analyzed and evaluated with EyeScore App. All DED patients obtained scores above 7, confirming their DED diagnosis. On the other hand, three controls obtained scores of 4 or 5, suggesting they were in the Pre-DED state with no noticeable clinical DED symptoms. All other controls obtain scores blow 3 with normal eye conditions. All tests were performed with the consents from the test participants. Conclusions Smart phone-based platform offers great potentials for point of care diagnostic methods. A POC enabled EyeScore App has been successfully developed for early diagnosis of DED. In the future, the authors plan to test EyeScore with a large-scale sample set of DED patients and controls to further validate the accuracy and predictability of eye healthiness score for its broader clinical applications.

Electroencephalography spectral coherence analysis during cycle ergometry in low- and high-tolerant individuals.

The main objective of this study was to further understanding of the patterns of spectral connectivity during exercise in low- and high-tolerant individuals. Thirty-nine healthy individuals (i.e., 17 low- and 22 high-tolerant participants) took part in the present study. A state-of-the-art portable electroencephalography system was used to measure the brain's electrical activity during an incremental exercise test performed until the point of volitional exhaustion on a cycle ergometer. Spectral coherence was used to explore the patterns of connectivity in the frontal, central, and parietal regions of the brain. Physiological, perceptual, and affective responses were assessed throughout the exercise bout. The spontaneous eyeblink rate was also calculated prior to commencement and upon completion of the exercise trial as an indirect assessment of the dopaminergic system. The present findings indicate that high-tolerant individuals reported lower levels of perceived activation, especially during the preliminary stages of the exercise test. Participants in the high-tolerance group also reported greater levels of remembered pleasure upon completion of the exercise test. The data also revealed that high-tolerant individuals exhibited increased connectivity of theta waves between frontal, central, and parietal electrode sites and increased connectivity of beta waves, primarily within the parietal cortex. Correlational analysis indicated the possibility that low- and high-tolerant individuals make use of different neural networks to process and regulate their psychophysiological state during exercise-related situations. This strategy could potentially represent a conscious decision to downregulate affective arousal and facilitate the neural control of working muscles during situations of physical stress.